Search Results (17,535)

Bamboo plantations are increasingly recognized as significant terrestrial carbon sinks, yet accurate estimation of biomass and carbon stocks requires species-specific, regionally validated allometric models. Bambusa emeiensis L.C.Chia & H.L.Fung (ci bamboo) is among the most ecologically and economically important clump-forming bamboo species in southwestern China, but robust multi-regional allometric models are lacking. Using destructive sampling data from 127 culms across two major production areas—Sichuan Province (n = 82) and Guizhou Province (n = 45)—we developed additive biomass and carbon storage model systems enforcing mathematical additivity via nonlinear seemingly unrelated regression (NSUR). Allometric equations used diameter at breast height (D), culm height (H), and compound variables (DH, D²H) as predictors. Regional models achieved R_a² of 0.0879–0.8320 total relative error (TRE): −0.99% to 0.04% for biomass and R_a² of 0.0923–0.8282 (TRE: −1.01% to 0.03%) for carbon storage; culm and total aboveground models attained R_a² ≥ 0.52. Organ-level carbon content (40.79%–44.46%) was significantly lower than the intergovernmental panel on climate change (IPCC) default of 50% (one-sample t-test, p < 0.01 for all organs), with Sichuan values exceeding Guizhou values (independent-samples t-test, p < 0.01), indicating that use of the default would overestimate carbon stocks by 12%–22%. Cross-regional validation revealed prediction biases of up to ±19.24% when applying single-region models outside their training area, whereas the combined model held errors within ±11.36% for biomass and ±8.49% for carbon storage. External validation using 32 independent culms from Hunan, Yunnan, and Chongqing confirmed the robustness of the combined model (TRE: −6.30% to 4.27%). A key limitation is that belowground biomass was not measured. The established models provide scientifically rigorous and practically applicable tools for regional carbon accounting of B. emeiensis plantations under China’s national greenhouse gas inventory framework and for informing sustainable bamboo management planning, and demonstrate that species- and region-specific carbon fractions are essential for accurate carbon stock assessments. Full article

Urbanization, together with shifts in rainfall patterns, has become an increasingly important driver of hydrological extremes in many rapidly developing tropical regions. In the Cimanceuri River Basin, Tangerang Regency, Indonesia, these processes have intensified over the last decade, raising concerns regarding flood risk. This study examines the combined influence of urban expansion and rainfall variability on flood dynamics over 2013–2025. Multi temporal land use classification based on Landsat imagery indicates a pronounced growth of impervious surfaces, primarily driven by rapid urban development and the conversion of agricultural land. To assess the hydrological consequences of these changes, rainfall–runoff processes and flood inundation were simulated using the Soil Conservation Service Curve Number (SCS–CN) method within a coupled HEC-HMS and HEC-RAS 2D modelling framework. Simulations were performed for multiple temporal conditions and design rainfall scenarios. Model calibration relied on observed flood events recorded in March 2025 in the Mustika Residential Area, Tangerang. The results suggest that urbanization has contributed to measurable increases in both peak discharge and inundation extent. Between 2013 and 2025, impervious surface coverage expanded by approximately 67%, accompanied by a rise in the composite Curve Number from 85.86 to 86.63 and an estimated 5.2% increase in flood extent. Also, the design rainfall increased from 85.01 to 90.95 with an average increase of 7.34%. Comparison between simulated inundation patterns and aerial imagery shows satisfactory agreement, with an average deviation of less than 10%, indicating acceptable model performance. Hydrologic analyses generated two discharge scenarios, consisting of event-based flow from the 5 March 2025 rainfall data and return-period flows derived from design rainfall under different rainfall-shift periods. The rainfall-shift analysis quantified changes in design rainfall and corresponding discharge using progressively updated rainfall records. Together, the results emphasize the combined effects of urban expansion and shifting rainfall patterns on flood dynamics, underscoring the need for adaptive land-use planning and climate-responsive water management in rapidly urbanizing catchments. Full article

Desert steppe is a typical ecosystem in arid and semi-arid regions and an important component of the global carbon cycle. Under the background of global climate change, the increasing frequency of extreme precipitation events and changes in precipitation patterns can significantly affect water- and heat-sensitive desert steppe ecosystems, thereby regulating soil CO₂ flux; however, the underlying mechanisms remain unclear. To investigate the effects of precipitation changes on soil CO₂ flux and their roles in carbon cycling and ecological sustainability, this study was conducted in a desert steppe. Seven precipitation treatments were established, including a control (CK) and ±15%, ±30%, and ±45% precipitation gradients. Based on the static chamber-gas chromatography method, combined with principal component analysis (PCA), correlation analysis, random forest modeling, and stepwise regression, the main influencing factors and their diurnal variation patterns of soil CO₂ flux were analyzed over 24 h periods from June to August. The results show that CO₂ flux ranged from −68.33 to 77.59 mg·m⁻²·h⁻¹. During the study period, CO₂ flux exhibited a diurnal pattern characterized by daytime emissions and weak nighttime emissions or uptake, along with clear seasonal variation. The ±30% precipitation treatment showed the largest fluctuation in CO₂ flux. Soil hydrothermal factors were identified as the key drivers of CO₂ flux. With changes in precipitation intensity, the combined effects of multiple factors increased ecosystem complexity, and the controlling factors showed clear seasonal differences. The results from different analytical methods were generally consistent, providing a reference for predicting CO₂ flux, developing carbon sink strategies, and supporting sustainable ecological management in desert steppe regions. Full article

Pinyon–juniper (PJ) woodlands, one of the most extensive mature and old-growth woodland types in the Western United States, provide critical ecological, cultural, and economic benefits but face increasing threats from climate change, altered disturbance regimes, invasive species, and pests. We developed the PJ Woodland Climate Adaptation Management Menu, a decision support tool designed to guide adaptive, climate-informed management of PJ ecosystems, particularly within the Colorado Plateau ecoregion. The menu was created through an iterative, collaborative process involving literature review, integration of strategies from existing adaptation frameworks, and extensive input from scientists, land managers, and community partners during workshops and focus groups. The menu links specific, evidence-based approaches to each of six broad strategies, including soliciting community input, mitigating disturbance, enhancing and maintaining biodiversity, conserving ecotones, timing actions for optimal outcomes, and accepting climate-driven changes when appropriate. It is intended for use with the Adaptation Workbook to help managers connect local goals and climate vulnerabilities to tailored management tactics. Hypothetical scenarios demonstrate the menu’s application to contrasting PJ woodland conditions, from die-off events to old-growth maintenance. Lessons learned during development underscore the value of early stakeholder engagement, cross-sector collaboration, and balancing diverse ecological objectives. This menu offers a flexible, transferable framework to strengthen climate resilience in PJ woodlands and serves as a model that could improve adaptation planning in other dryland forest ecosystems. Full article

Mangroves are important coastal wetland ecosystems with high ecological service values and strong carbon sequestration capacity, serving as a crucial barrier for coastal ecological security. However, current afforestation efforts often ignore environmental suitability differences among mangrove species, while the applicability value and ecological risks of exotic species (Laguncularia racemosa and Sonneratia apetala) for restoration remain poorly understood. Five native and two exotic mangrove species along China’s coasts were selected in this study. Using the MaxEnt model, we identified key environmental factors governing their distribution, predicted their current and future suitable habitats (under the SSP245 scenario in the 2070s), and quantified niche overlap between native and exotic mangroves. The results showed that temperature-related factors (air and sea temperature) are the core climatic drivers shaping the typical mangrove distribution, followed by sea surface salinity, with precipitation contributing little. Currently, niche overlap between native and the two exotic species is low (D.overlap: 0.129–0.340), indicating certain niche differentiation. Under the SSP245 scenario in the 2070s, except for Rhizophora stylosa, other studied species appear to experience expanded suitable habitat areas and a northward latitudinal distribution shift. Compared with Sonneratia apetala, Laguncularia racemosa exhibits a more pronounced expansion of suitable habitats in the future, with its overall suitable area second only to the native Kandelia obovata, indicating its stronger adaptive potential to climate change. Clarifying niche differentiation and constructing species-specific management frameworks may facilitate biological invasion control, mangrove restoration, and species diversity improvement. Full article

Soil erosion and nutrient loss degrade the soil resource base and water quality in dryland agricultural landscapes, yet optimal design of vegetation buffers for soil conservation under intensifying rainfall remains poorly quantified, particularly for nutrient retention. This study is novel in integrating event-scale rainfall-simulation experiments, Bayesian hierarchical modelling, Causal Forest analysis, and WEPP simulations to quantify how the sequential addition of biocrusts and grasses to shrub buffers shifts density thresholds for runoff, soil loss, and nutrient export across varying rainfall intensities. Experiments were conducted across a continuous shrub-density gradient (0–11,429 plants ha⁻¹) representing three configurations: shrub monoculture, shrub-biocrust, and shrub-biocrust-grass on agricultural hillslopes of the Chinese Loess Plateau. Runoff, soil loss, and exports of total nitrogen (TN) and total phosphorus (TP) were measured. Results demonstrate three main findings. First, multilayer shrub–biocrust–grass buffers exhibited lower soil loss than monocultures. Posterior estimates indicate reductions from approximately 3.8 t ha⁻¹ at moderate monoculture density to below 1.0 t ha⁻¹ at lower planting densities, with 94% of the highest-density intervals reflecting uncertainty in these estimates. Second, Causal Forest analysis reveals a functional separation of controls: rainfall intensity dominates soil loss (88% importance) and runoff (84%), whereas nutrient retention responds more strongly to buffer structure and density management. Third, WEPP simulations across rainfall intensities (50–180 mm h⁻¹) and slopes (10–30%) identify an optimal multilayer buffer density of 3800–5700 plants ha⁻¹, which delivers robust multifunctional benefits with 50–67% lower planting requirements than conventional high-density monocultures. These findings demonstrate that multilayer vegetation buffers enhance soil retention and reduce nitrogen and phosphorus losses from hillslopes, sustaining the soil resource base and protecting water quality in dryland agricultural landscapes. The integrated modelling framework provides transferable, evidence-based density recommendations for climate-resilient soil conservation in similar dryland regions. Full article

Carbon is an essential component in the regulation of climate systems through the global biogeochemical cycle. However, changes in land use/land cover (LULC) have reduced the capacity of terrestrial ecosystems like watershed to store carbon. This shows the need for a policy framework that balances conservative objectives with agricultural demands, as watersheds are required to support carbon storage and food production. Previous studies have generally assessed carbon dynamics or LULC change separately, with limited integration of policy-driven scenarios. Therefore, this study aimed to conduct multi-scenario carbon storage modeling to evaluate LULC protection strategies in the Cimanuk Watershed, Indonesia, an area experiencing significant LULC pressures. The method used consisted of Support Vector Machine (SVM)–Markov, the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST), Geodetector, and Getis-Ord Gi*. A total of four scenarios were used to project LULC and carbon storage in 2042, which included Business as Usual (BAU), Paddy Field Protection (PFP), Forest Protection (FOP), and Paddy Field and Forest Protection (PFFOP). The results showed that forest area declined by 39,400 ha between 2015 and 2025, thereby reducing carbon storage. The PFFOP scenario was identified as the most viable, combining the protection of paddy fields and forests to balance agricultural production and carbon sequestration. Among the factors analyzed, slope exerted the greatest influence on carbon storage. Spatial cluster analysis showed that carbon hotspots were predominantly located in the upper Cimanuk sub-watershed. These results offered valuable insights into scenario-based sustainable watershed management to optimize carbon storage and maintain agricultural function. Furthermore, the proposed framework showed promising potential for application in other tropical watersheds, serving as a reference for decision-makers in sustainable watershed management. Full article

The rice leaf roller (RLR), Cnaphalocrocis medinalis (Guenée), is a major migratory pest that threatens rice production across East Asia. Effective management of migratory pests relies fundamentally on accurately identifying their source areas, population dynamics, and key environmental drivers. Western Hunan is a critical rice-growing region characterized by unique topography and varied climates, making it a principal pathway for RLR migration. Based on 14-year (2011–2024) monitoring datasets, we identified substantial interannual variability in July RLR abundance in Western Hunan, when the population typically peaks, highlighting the episodic and unstable nature of regional infestations. Back-trajectory simulations reveal that heavy occurrence years of RLR feature clear northward migration pathways from the Indo-China Peninsula and South China to Western Hunan in July, supported by strong southerly winds along the route. Multiple linear regression analysis further shows that spring warmth initially facilitates high population accumulation in source regions, and the synergistic effect of source-region precipitation deficits and abundant local rainfall triggers large-scale immigration into Western Hunan. These meteorological factors collectively account for up to 66% of the interannual variability in RLR population fluctuations, confirming that climatic conditions largely determine outbreak severity. This provides a robust quantitative framework for regional early-warning systems and sustainable pest management in migratory corridors. Full article

In the context of global warming, the South China Sea is showing an accelerating warming trend, which highlights the impact of climate change on the marine environment of the South China Sea. The interdecadal variation of chlorophyll a concentration (Chl_a) directly reflects the long-term evolution pattern of the upper marine ecological environment in the South China Sea and has significant indicative significance for marine ecological protection. This study investigated the relationships between Chl_a concentration, sea surface temperature (SST), shortwave radiation (SSRD), mixed layer depth (MLD), wind speed (Wind), and geostrophic current (ugo) in the South China Sea over the past 27 years. Statistical methods were used to analyze the differentiated impacts of marine environmental factors under different climate backgrounds on Chl_a concentration. From 1998 to 2007 (P1), there was a decreasing trend in the early stage, from 2007 to 2015 (P2), there was an upward trend in the middle period, and from 2015 to 2024 (P3), there was an upward trend in the recent period. During these three stages, SST and MLD were the core influencing factors. The threshold of SST gradually increased over time, reaching 27.05 °C, 27.24 °C, and 27.32 °C, respectively. The average normalized information flow (NIF) of Chl_a concentration changed from positive to negative. In the early stage of P1, the SST in most areas of the South China Sea was less than 27.05 °C, and in the P2 and P3 periods, the SST in most areas reached the threshold. The thresholds of MLD were 22.33 m, 21.64 m, and 33.68 m, respectively. The average NIF of Chl_a concentration was positive in all periods. The MLD in most areas of the South China Sea did not reach the threshold in all three periods. These findings emphasize the different roles of marine environmental factors in regulating Chl_a concentration in the South China Sea, providing a scientific basis for the ecological health monitoring of marine fishery areas, disaster warning, and adaptive management in response to climate warming. Full article

Freezing rain is a high-impact winter weather phenomenon that acts as a major disturbance agent in forest ecosystems, causing canopy damage, stem breakage, tree mortality, and long-term changes in forest structure and functioning. Although ice storms have been studied for decades, research on freezing rain impacts on forests remains fragmented across multiple disciplines, and few studies have attempted an integrated synthesis that simultaneously combines climatological, ecological, and methodological perspectives. In this study, we present a systematic and integrative review of the scientific literature on freezing rain and forests, combining a large-scale bibliometric analysis with an in-depth qualitative synthesis. A total of 241 publications retrieved from the Scopus and Web of Science databases were analyzed following PRISMA guidelines. The bibliometric assessment examined publication trends, geographic distribution, institutional contributions, research domains, and keyword networks. The qualitative review synthesized current knowledge on freezing rain climatology, forest damage mechanisms, species-specific vulnerability, major ice storm events, detection and modeling approaches, and ecological consequences. Results reveal a strong increase in scientific output over the last two decades, dominated by research from North America and northern Europe. Ice accretion intensity emerges as the primary driver of forest damage, while species traits, crown architecture, tree size, stand structure, topography, and exposure strongly modulate damage severity. Freezing rain affects a wide range of forest types worldwide and triggers both immediate structural damage and long-term ecological effects, including altered successional dynamics and reduced forest productivity. Recent methodological advances—including passive remote sensing (e.g., optical satellite data), active remote sensing (e.g., LiDAR), experimental ice storm simulations, reanalysis datasets, and machine learning approaches—have significantly improved detection, monitoring, and forecasting capabilities. Despite these advances, major research gaps remain, particularly regarding long-term ecosystem recovery, trait-based vulnerability, socio-economic impacts, and future freezing rain regimes under climate change. This review highlights freezing rain as an increasingly important but underappreciated forest disturbance and underscores the need for interdisciplinary research and adaptive management strategies in ice-prone regions. Full article

Climate-related extremes such as floods and droughts have been the main causes of natural disasters in southern Africa in recent years, with noticeable trends in climate extremes being observed. The Limpopo Province in South Africa has been especially prone to these extremes. The extreme precipitation in Limpopo is mainly caused by a mix of intense tropical weather systems and La Niña conditions, both exacerbated by climate change. Climate change exacerbates current water challenges across the province by affecting precipitation patterns, distribution, timing and intensity, leading to extreme climate events such as floods and drought. The historical and future trends of precipitation and relevant extreme indices using observed data from the South African Weather Service and CORDEX ensemble model simulations under the RCP4.5 and RCP8.5 scenarios were examined. An analysis of all precipitation data suitable for the study of long-term variability and trends indicates that most areas underwent drying to various degrees over the last century, especially the central and western parts. Drier conditions over the eastern parts have become more prevalent over the last 50 years. Also, more extremes on a sub-seasonal basis were experienced. Regarding future scenarios, three projected time periods compared to the baseline period (1976–2005) were examined: Current climatology (2006–2035), near-future (2036–2065), and far-future (2066–2095). Most areas will experience a further decrease in precipitation under both emission scenarios, especially in the south-east, central and extreme northern parts. In addition, these areas are expected to experience a decrease in the frequency of heavy precipitation days for all periods under both RCP scenarios, mainly due to drying. Consecutive dry days are expected to increase significantly. Transitioning to renewable energy and enhancing natural carbon sinks can reduce emissions, while prioritizing resilience through renewable energy, water management, and climate-smart agriculture will help address climate change challenges in the province. Full article

Sustainable groundwater management represents a main goal for the future in the context of climate change and increasing anthropogenic pressure. In recent decades, intrinsic vulnerability assessment and risk mapping have been established as some of the most important tools for groundwater preservation, but they have also shown limitations due to their static nature and their failure to account for the inherent uncertainty of hydrogeological parameters. This study proposes an innovative hybrid framework that integrates traditional overlay-index methodology (SINTACS Release 5) with stochastic numerical modeling to assess groundwater contamination risk and evolve it into a dynamic time-dependent tool. This methodology was applied to a case study of the Lapisina Valley phreatic aquifer (Northeastern Italy), a strategic area for drinking water supply. Numerical simulations were implemented to reproduce groundwater flow using the MODFLOW-NWT code. To address parametric uncertainty, 237 stochastic realizations of the modeling domain were generated using the Latin Hypercube Sampling method, randomizing hydraulic conductivity values. Advective transport was simulated through forward particle tracking using the MODPATH code, starting from the identified and classified hazard sources within the study area. Assuming the absence of attenuation during transport allowed for a conservative worst-case scenario. The result was the definition of a probabilistic contaminant propagation factor, a time-dependent indicator that quantifies the probability of pollution arrival to a specific discrete portion of the domain. This probabilistic factor was combined with three indexes commonly utilized for risk assessment (the intrinsic vulnerability index, hazard index, and value of the resource) to generate four contamination risk maps representing different timestep scenarios (5, 10, 20, and 50 years) after the arrival of a hypothetical contaminant in the saturated zone. This approach transforms risk mapping from being a useful but static snapshot to a predictive dynamic framework. Full article

Soil–Forage–Livestock systems (SFL-systems) integration is fundamental for sustainable land management in arid lands, where conventional crop production is often unfeasible. Aridisols dominate dryland agroecosystems and their edaphic constraints, together with climatic limitations, constitute a major bottleneck for fertility and productivity in key arid regions worldwide. This narrative review provides a taxonomic and edaphic framework to guide sustainable SFL-systems and integrates current approaches and technological applications for forage production in arid environments, focusing on an edaphic-digital scheme that combines organic and inorganic soil amendments with AI-based decision support to improve Aridisols productivity and resilience. Searches of the literature (ScienceDirect, EBSCOhost, Clarivate Web of Science; English, 2021–2025) screened 309 records and selected 169 references; seminal older works were consulted for context. Representative quantitative outcomes from the reviewed literature include SOC increases of ~15–30% after multi-year organic inputs; forage biomass gains of ~10–25% following amendments that correct sodicity; and water-productivity improvements up to ~30% with hydrogels or biochar. AI tools can improve soil diagnostics and amendment selection (diagnostic accuracy improvements of ~15–30% in recent studies) and generate predictive models of amendment–response that facilitate optimization of application rates and water use. The novel contribution of this review is the explicit linkage of SFL-systems and amendment-based soil restoration with AI-driven diagnostics and decision support, providing actionable metrics and research priorities to translate digital diagnostics into measurable forage gains in arid and semi-arid regions. Overall, the evidence suggests that targeted soil restoration, reinforced by AI-based support systems, is a feasible strategy to increase forage availability and ecosystem service provision in drylands. Full article

Cover crops in woody crops as a sustainable land management alternative to conventional tillage induce changes in soil properties that improve ecosystem functioning. Soil is highly dynamic, and disturbances in environmental conditions affect soil microorganisms, particularly in gypsiferous soils, where microbiological activity remains poorly understood. This study evaluated the effects of three cover crop systems: spontaneous permanent vegetation cover (SVE), annual legume cover (VIC), and permanent grass cover (BRA), compared with conventional tillage (TIL), on soil physicochemical and biological properties in an olive grove over two crop seasons. Overall, cover crops promoted higher microbial activity and carbon storage than tillage, with responses being more pronounced during the wetter year. Conventional tillage consistently exhibited the lowest levels of enzyme activities and carbon stocks, whereas permanent covers showed stronger positive effects on soil functioning. These findings indicate that the benefits of cover crops on soil processes are reinforced under favorable moisture conditions but also remain under drier years, highlighting their stabilizing role. The improvement of soil health induced by cover crops contributes to enhancing soil ecosystem services, including soil fertility, in olive groves, supporting their adoption as a sustainable management strategy in Mediterranean agroecosystems, even under lower rainfall conditions. Full article

Driven by increasing demand in the health and wellness industry, Traditional Chinese Medicine (TCM) agriculture currently faces significant challenges related to supply–demand imbalances and continuous cropping obstacles (CCOs). Intercropping and crop rotation can mitigate yield decline and environmental stress by improving microclimates and rhizosphere ecology. However, there is still a lack of bibliometric synthesis within this research area. To analyze research hotspots and evolutionary trends, 192 articles on the intercropping and crop rotation of medicinal plants were collected from the Web of Science Core Collection (1998–2025), including databases such as the Science Citation Index Expanded (SCIE), the Social Science Citation Index (SSCI) and the Conference Proceedings Citation Index (CPCI). The results revealed a steady increase in publication volume over time. China emerged as the most prolific contributor (93 articles), while the United States occupied a pivotal position in the global collaborative network, achieving a high centrality of 0.90. Research hotspots in this field have evolved from an early emphasis on plant yield and quality toward the mechanisms for alleviating CCOs, interspecific interactions within the rhizosphere microbiome, and the ecological management of soil health. Keyword bursts indicate that “microbial community” and “carbon” have emerged as the current research frontiers. To clarify the micro-mechanisms by which intercropping and crop rotation patterns mitigate or prevent CCOs, future research should prioritize the integration of multi-omics approaches to resolve molecular interactions within the “microbe–plant–soil” nexus. Key priorities include the development of functional Synthetic Microbial Communities (SynComs) and the establishment of comprehensive evaluation systems for ecological cultivation. Furthermore, aligning these models with global climate neutrality strategies would facilitate the balance between high-quality medicinal production and ecosystem stability. Full article